
#Library loading
library(monocle3)
library(Seurat)
library(SeuratWrappers)
library(ggplot2)
library(dplyr)
library(patchwork)
library(magrittr)

#http://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/monocle3.html 참조
#https://satijalab.org/signac/articles/monocle.html

Loading required package: Biobase

Loading required package: BiocGenerics

Loading required package: parallel


Attaching package: ‘BiocGenerics’


The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB


The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs


The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min


Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Loading required package: SingleCellExperiment

Loading required package: SummarizedExperiment

Loading required package: MatrixGenerics

Loading required package: matrixStats


Attaching package: ‘matrixStats’


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    anyMissing, rowMedians


Attaching package: ‘MatrixGenerics’


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    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
    colWeightedMeans, colWeightedMedians, colWeightedSds,
    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
    rowWeightedSds, rowWeightedVars


The following object is masked from ‘package:Biobase’:

    rowMedians


Loading required package: GenomicRanges

Loading required package: stats4

Loading required package: S4Vectors


Attaching package: ‘S4Vectors’


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    expand.grid


Loading required package: IRanges

Loading required package: GenomeInfoDb


Attaching package: ‘monocle3’


The following objects are masked from ‘package:Biobase’:

    exprs, fData, fData<-, pData, pData<-


Attaching SeuratObject


Attaching package: ‘Seurat’


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    Assays


Attaching package: ‘dplyr’


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    intersect, setdiff, union


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    intersect


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    collapse, desc, intersect, setdiff, slice, union


The following objects are masked from ‘package:S4Vectors’:

    first, intersect, rename, setdiff, setequal, union


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    count


The following object is masked from ‘package:Biobase’:

    combine


The following objects are masked from ‘package:BiocGenerics’:

    combine, intersect, setdiff, union


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union


#Plot size 조정
options(repr.plot.width = 8, repr.plot.height = 8, repr.plot.res = 600)


# Load the data from seurat rds
Epi.integrated <- readRDS('/home/minho2/Epi2_integrated.rds')


# Transfer seurat cluster info
list_cluster <-  Epi.integrated@meta.data[['seurat_clusters']]
names(list_cluster) <-  Epi.integrated@assays[["RNA"]]@data@Dimnames[[2]]
list_cluster


# Transfer seurat cluster info
cds <- as.cell_data_set(Epi.integrated)
cds <- cluster_cells(cds = cds, reduction_method = "UMAP")

cds@clusters@listData[["UMAP"]][["clusters"]] <- list_cluster
cds@clusters@listData[["UMAP"]][["louvain_res"]] <- "NA"

Warning message:
“Monocle 3 trajectories require cluster partitions, which Seurat does not calculate. Please run 'cluster_cells' on your cell_data_set object”


# Dimplot
p1 <- plot_cells(cds, show_trajectory_graph = FALSE)
p2 <- plot_cells(cds, color_cells_by = "partition", show_trajectory_graph = FALSE)
wrap_plots(p1, p2)


# Building trajectories with Monocle 3 it will take a long times
cds <- learn_graph(cds, use_partition = TRUE)

  |======================================================================| 100%

Warning message in min(data_df$weight[data_df$weight > 0]):
“no non-missing arguments to min; returning Inf”


# dimplot with monocle3 trajectory
plot_cells(cds, 
           label_groups_by_cluster = TRUE, 
           label_leaves = TRUE, 
           label_branch_points = TRUE,
           graph_label_size=1.5)


# Select starting point with gene name
max.avp <- which.max(unlist(FetchData(Epi.integrated, "Gzma")))
max.avp <- colnames(Epi.integrated)[max.avp]
cds <- order_cells(cds, root_cells = max.avp)
plot_cells(cds, color_cells_by = "pseudotime", 
           label_cell_groups = TRUE, 
           label_leaves = TRUE, 
           label_branch_points = TRUE)

Cells aren't colored in a way that allows them to be grouped.


plot_cells(cds, reduction_method= "UMAP")

cds

class: cell_data_set 
dim: 2000 19339 
metadata(1): citations
assays(2): logcounts counts
rownames(2000): Spp1 Hspb1 ... Tgm1 Cgnl1
rowData names(0):
colnames(19339): AAACCCAAGCAACCAG-1_1 AAACCCAAGGACTGGT-1_1 ...
  TTTGTTGTCTCTCTAA-1_2 TTTGTTGTCTTTGGAG-1_2
colData names(8): orig.ident nCount_RNA ... seurat_clusters ident
reducedDimNames(2): PCA UMAP
altExpNames(0):